Search

Purpose: We characterized the diagnostic properties of serial percent free prostate specific antigen in relation to prostate specific antigen in a multiethnic, multiracial cohort of healthy men., Materials and Methods: A total of 6,982 percent free prostate specific antigen and prostate specific antigen measurements were obtained from participants in a greater than 12-year Texas screening study comprising 1,625 men who never underwent biopsy, 497 who underwent 1 or more biopsies negative for prostate cancer and 61 diagnosed with prostate cancer. We evaluated the ROC AUC of percent free prostate specific antigen and the proportion of patients with fluctuating values across multiple visits determined according to 2 thresholds (less than 15% vs 25%). The proportion of cancer cases in which percent free prostate specific antigen indicated a positive test before prostate specific antigen greater than 4 ng/ml did and the number of negative biopsies that would have been spared by negative percent free prostate specific antigen test results were calculated., Results: Percent free prostate specific antigen fluctuated around its threshold of less than 25% (less than 15%) in 38.3% (78.1%), 42.2% (20.9%), and 11.4% (25.7%) of patients never biopsied, and with negative and positive biopsies, respectively. At the same thresholds, percent free prostate specific antigen tested positive earlier than prostate specific antigen in 71.4% and 34.2% of cancer cases, respectively. Among men with multiple negative biopsies and PSA greater than 4 ng/ml, percent free PSA would have tested negative in 31.6% and 65.8%, respectively., Conclusions: Percent free prostate specific antigen should accompany prostate specific antigen testing to potentially spare unnecessary biopsies or detect cancer earlier. When near the threshold, both tests should be repeated due to commonly observed fluctuation., (Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.)

Purpose: Prostate specific antigen screening is controversial, as a large number of men must be screened annually to achieve a benefit. We sought to determine whether baseline prostate specific antigen could reliably predict subsequent risk of prostate cancer and risk of consequential prostate cancer., Materials and Methods: A multiethnic cohort of 2,923 prostate cancer-free men was recruited between 2000 and 2012, and followed for a median of 7.5 years. Baseline prostate specific antigen was stratified into 6 strata and relative hazards of prostate cancer detection for each prostate specific antigen stratum were estimated, adjusting for ethnicity, family history and age., Results: During followup 289 patients were diagnosed with prostate cancer. Men with baseline prostate specific antigen in the lowest stratum (0.1 to 1.0 ng/ml) were at greatly reduced risk for prostate cancer during followup. This half of the cohort with prostate specific antigen 1.0 ng/ml or less were at 3.4% (95% CI 2.1, 4.5) 10-year risk of prostate cancer and 90% of the cancers were low risk. By comparison the other half were at 15% to 39% risk of cancer detection with a 39% risk in the highest stratum (3 to 10 ng/ml)., Conclusions: Optimal prostate specific antigen screening frequency for men with a prostate specific antigen level of 0.1 to 1.0 ng/ml may be up to every 10 years. This approach has the potential to dramatically reduce the cost of screening, decreasing over detection of inconsequential tumors, while maintaining detection of tumors for which treatment has been proven to reduce prostate cancer mortality., (Copyright © 2015 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.)

Background: A model was built that characterized effects of individual factors on five-year prostate cancer (PCa) risk in the Prostate, Lung, Colon, and Ovarian Cancer Screening Trial (PLCO) and the Selenium and Vitamin E Cancer Prevention Trial (SELECT). This model was validated in a third San Antonio Biomarkers of Risk (SABOR) screening cohort.Methods: A prediction model for 1- to 5-year risk of developing PCa and Gleason > 7 PCa (HG PCa) was built on PLCO and SELECT using the Cox proportional hazards model adjusting for patient baseline characteristics. Random forests and neural networks were compared to Cox proportional hazard survival models, using the trial datasets for model building and the SABOR cohort for model evaluation. The most accurate prediction model is included in an online calculator.Results: The respective rates of PCa were 8.9%, 7.2%, and 11.1% in PLCO (n = 31,495), SELECT (n = 35,507), and SABOR (n = 1790) over median follow-up of 11.7, 8.1 and 9.0 years. The Cox model showed higher prostate-specific antigen (PSA), BMI and age, and African American race to be associated with PCa and HGPCa. Five-year risk predictions from the combined SELECT and PLCO model effectively discriminated risk in the SABOR cohort with C-index 0.76 (95% CI [0.72, 0.79]) for PCa, and 0.74 (95% CI [0.65,0.83]) for HGPCa.Conclusions: A 1- to 5-year PCa risk prediction model developed from PLCO and SELECT was validated with SABOR and implemented online. This model can individualize and inform shared screening decisions. [ABSTRACT FROM AUTHOR]

This research is motivated from the data from a large Selenium and Vitamin E Cancer Prevention Trial (SELECT). The prostate specific antigens (PSAs) were collected longitudinally, and the survival endpoint was the time to low-grade cancer or the time to high-grade cancer (competing risks). In this article, the goal is to model the longitudinal PSA data and the time-to-prostate cancer (PC) due to low- or high-grade. We consider the low-grade and high-grade as two competing causes of developing PC. A joint model for simultaneously analysing longitudinal and time-to-event data in the presence of multiple causes of failure (or competing risk) is proposed within the Bayesian framework. The proposed model allows for handling the missing causes of failure in the SELECT data and implementing an efficient Markov chain Monte Carlo sampling algorithm to sample from the posterior distribution via a novel reparameterization technique. Bayesian criteria, Δ DIC Surv , and Δ WAIC Surv , are introduced to quantify the gain in fit in the survival sub-model due to the inclusion of longitudinal data. A simulation study is conducted to examine the empirical performance of the posterior estimates as well as Δ DIC Surv and Δ WAIC Surv and a detailed analysis of the SELECT data is also carried out to further demonstrate the proposed methodology. [ABSTRACT FROM AUTHOR]

Background: Finasteride, a 5-alpha reductase inhibitor may have effects on biomarkers such as prostate-specific antigen (PSA) that could be leveraged to improve screening. Objective: To determine the predictive characteristics of biomarkers for prostate cancer for cancer on biopsy following 3 months of finasteride use compared with placebo. Design, setting and participants: 383 men from multiple clinical sites with intermediate prostate cancer risk, without history of prostate cancer, were randomly allocated in a double-blinded manner, 4:1, to receive either finasteride or placebo for 90 days at which time a prostate biopsy was performed. Outcome measurements and statistical analysis: The primary outcomes were associations of biomarkers with prostate cancer that were tested using multiple logistic regression and area under the receiver operating curves (AUC). Biomarkers for PCA risk (PCA3, TMPRSS2:ERG (T2:ERG) gene product, and PSA) were measured at baseline and at biopsy in a blinded fashion to assess the predictive performance of baseline levels, 90-day levels, and measures of change relative to standard predictors. Results and limitations: A total of 292 (233 finasteride; 59 placebo) randomized patients underwent biopsy and were analyzed. On finasteride, baseline and 90-day measures of PCA3 and T2:ERG had similar moderate discrimination capacity with AUCs 62 to 65% (p-values < 0.001 and 0.001, respectively), but their rates of change had no discrimination ability (AUC 51%, (95% CI 43 to 60% p = 0.72) and 48% (95% CI 44 to 60%, p = 0.62), respectively).) Relative to baseline, the 90-day PCA3 and PSA decreased in the finasteride group by 25% and 50%, respectively (both p<0.001). T2:ERG had a smaller, non-significant change post finasteride treatment (p = 0.08). Conclusions: Short-term finasteride therapy did not improve performance of the most commonly-employed prostate cancer biomarkers. Threshold values for new biomarkers of prostate cancer should be interpreted with caution in patients receiving finasteride until formal validation of test performance in these patients is conducted. Patient summary: Three months of finasteride treatment did not increase the accuracy for predicting the outcome on prostate biopsy but did have a significant effect on biomarker values. Adjustments to thresholds for biopsy for men on finasteride are proposed. Trial registration: ClinicalTrials.gov, . [ABSTRACT FROM AUTHOR]